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ObjectivesPoor adherence to medication following pediatric liver transplantation remains a major challenge, with some estimates suggesting that 50% of adolescent liver transplant recipients exhibit reduced medication adherence. To date, no gold standard has emerged to address this challenge; however, system interventions are most likely to be successful. We sought to implement a system to identify and address adherence barriers in a liver transplant clinic.MethodsUsing structured quality improvement methods, including multiple plan-do-study-act cycles, we developed a system to screen for patients at risk of poor adherence, identify patient- and/or parent-reported barriers to adherence, and partner with patients to overcome identified barriers. We developed a process to track key outcomes, including the variability in tacrolimus trough levels and episodes of late acute cellular rejection.ResultsThe practice saw a total of 85 patients over 6 months, and about half were females. Over this period, the improvement team implemented this system-level process with high reliability (>90% of patients received the bundle of interventions). The most commonly identified adherence barrier by patients and caregivers was "forgetting." The second most commonly identified adherence barrier by patients was that the medication "gets in the way of their activities," whereas by caregivers, it was "difficulty swallowing pills."DiscussionWe identified challenges and opportunities to screen for poor adherence and identify patient- and/or caregiver-reported barriers to immunosuppression adherence. Identifying such barriers and partnering with patients to overcome those barriers using patient-centered, barrier-specific interventions could improve long-term graft survival through improved medication adherence.

Machine learning analyses allow for the consideration of numerous variables in order to accommodate complex relationships that would not otherwise be apparent in traditional statistical methods to better classify patient risk. The SPLIT registry data were analyzed to determine whether baseline demographic factors and clinical/biochemical factors in the first-year post-transplant could predict ideal outcome at 3 years (IO-3) after LT. Participants who received their first, isolated LT between 2002 and 2006 and had follow-up data 3 years post-LT were included. IO-3 was defined as alive at 3 years, normal ALT (